The main focus of this work is to introduce a memory-efficient model for dynamic MRI called Deep Bi-Linear Unsupervised Representation (DEBLUR). This approach exploits the power of CNN to further improve the performance while retaining the memory efficiency of bilinear representations. We use the structural bias of CNN networks [1]} as priors on the factors; this approach is an alternative to current schemes that penalize energy [2]}, sparsity [3]}, or kernel priors [4]}, [5]} on the factors. In particular, we assume that the spatial and temporal factors are generated by two CNN generators whose parameters are estimated from the measured data. The CNN parameters are learned such that the k-space measurements of the bilinear representation matches the multi-channel measurements. The ability of the proposed scheme to directly learn the compressed representation makes it more memory efficient in multidimensional applications compared to current CNN approaches [6]}, [7]}, [8]}.
